Dual Error Bounded Trajectory Simplification

Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is well-known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are effective approaches to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. LS algorithms in general use the perpendicular Euclidean distance (PED) or synchronous Euclidean distance (SED) of a data point to a proposed generalized line as the condition to discard or retain that data point. In the observation that the PED approach performances well in terms of compression ratios but is not suitable for temporal-spatio queries, while the SED approach is on the contrary, this paper presents a dual distances checking approach that leverages the benefits of approaches PED and SED, and satisfies the varied distance checking requirements. We experimentally verify that our approach is flexible and effective, using two real-life trajectory datasets.